RE: $COV UNCONDITIONAL PRINT=E
Sam:
The nature of the $COV step using classical NONMEM methods is it can
take longer than the $EST command at times. There is no infinite loop,
it just has to evaluate OBJ often in cases of large problems. The SIGL
does not help with this, and the ctrl-K and ctrl-E switches do not act
during evaluation of the $COV STEP. advan13 is best used for the Monte
Carlo methods. If you are not particular about getting $COV results by
classical NONMEM methods, you can ctrl-C.
Regarding BAYES analysis, the release notes, and the slides I gave at
the workshops (make stable programs section of slides), inform the user
that Monte Carlo methods can call the user model with very extreme
values. Thus, insert protective code, the kind that avoid square
rooting negative numbers, exponentiating very large numbers, etc.
For new methods, use INTERACTION, even when error is homoscedastic.
Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics
ICON Development Solutions
Tel: (215) 616-6428
Mob: (925) 286-0769
Email: [email protected]
Web: www.icondevsolutions.com
Quoted reply history
________________________________
From: [email protected] [mailto:[email protected]]
On Behalf Of Sam Liao
Sent: Monday, October 26, 2009 11:36 AM
To: [email protected]; [email protected]
Subject: [NMusers] $COV UNCONDITIONAL PRINT=E
Dear NONMEM team:
I have two nm7 questions.
I have a PKPD model using ADVAN13 to solve the ODE. It took 12 hours to
complete the $EST step with rounding error.
But it's been over 15 hours running the $COV step with UNCONDITIONAL
option.
This is much longer time than I expected. Could this be an infinitive
loop? Should I interrupt the run and try smaller SIGL in $COV? I
tried the CTL-K or CTL-E to exit, but it did not work for me.
The second question related to BAYES method in $EST step. The model run
successfully using FOCE method. But when I tried the BAYES method with
NOABORT option, it terminated with 'OBJECTIVE FUNCTION IS INFINITE' in
Burn-in mode after 10 iterations. The statement used as below. How can
I get around this problem?
=======================
$SUBROUTINES ADVAN13 TOL=6
$EST METHOD=BAYES NBURN=100 NSAMPLE=3000 SIGL=6 NOABORT FILE=RUN136.TXT
Best regards,
Sam
From: [email protected] [mailto:[email protected]]
On Behalf Of Bauer, Robert
Sent: Sunday, October 25, 2009 11:29 PM
To: [email protected]; [email protected]
Subject: RE: [NMusers] method=ITS, OPTIMIZATION NOT TESTED (?!)
Pavel:
The objective function progress looks good. You should expect some
Monte Carlo fluctuations. You should also run more iterations (perhaps
NITER=200), and set CTYPE=3, which turns on the termination tester. To
resume where you left off, rename your new control stream file, and put
in the following lines.
$EST METHOD=CHAIN NSAMPLE=0 ISAMPLE=50
FILE=my_old_control_stream_file.ext
$EST METHOD=IMP NITER=200 CTYPE=3 FILE=my_new_control_stream_file.ext
Make sure you are linear MU referencing to get the greatest efficiency.
Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics
ICON Development Solutions
Tel: (215) 616-6428
Mob: (925) 286-0769
Email: [email protected] <mailto:[email protected]>
Web: www.icondevsolutions.com
________________________________
From: [email protected] [mailto:[email protected]]
On Behalf Of [email protected]
Sent: Saturday, October 24, 2009 8:03 PM
To: [email protected]
Subject: [NMusers] method=ITS, OPTIMIZATION NOT TESTED (?!)
Hello NONMEM Team,
I found method=imp useful when there are local maxima. Nevertheless, at
the end of optimization it prints a message, which makes me feel
somewhat uncomfortable: OPTIMIZATION NOT TESTED. Also, the final
objective function is not always the lowest one. An example is below.
How do we interpret the results in this case?
THETAS THAT ARE SIGMA-LIKE:
MONITORING OF SEARCH:
iteration 0 OBJ= 10625.663135214874
iteration 1 OBJ= 10601.188754983375
iteration 2 OBJ= 10537.895114114934
iteration 3 OBJ= 10471.674625518765
iteration 4 OBJ= 10430.297437731866
iteration 5 OBJ= 10461.973668565577
iteration 6 OBJ= 10462.638834406265
iteration 7 OBJ= 10423.464983371641
iteration 8 OBJ= 10417.959956991735
iteration 9 OBJ= 10417.594007447198
iteration 10 OBJ= 10414.708468642830
iteration 11 OBJ= 10427.810693855947
iteration 12 OBJ= 10412.889081059604
iteration 13 OBJ= 10411.980622268416
iteration 14 OBJ= 10424.501127174915
iteration 15 OBJ= 10416.332869468861
iteration 16 OBJ= 10416.622580251338
iteration 17 OBJ= 10412.401585537709
iteration 18 OBJ= 10415.117257355550
iteration 19 OBJ= 10415.302370961055
iteration 20 OBJ= 10409.066188189252
iteration 21 OBJ= 10413.780620468329
iteration 22 OBJ= 10410.787496174480
iteration 23 OBJ= 10410.633582415931
iteration 24 OBJ= 10409.970257443048
iteration 25 OBJ= 10409.702420124611
iteration 26 OBJ= 10409.213115058612
iteration 27 OBJ= 10409.690639357370
iteration 28 OBJ= 10410.016047785200
iteration 29 OBJ= 10408.157468814226
iteration 30 OBJ= 10407.779614704938
iteration 31 OBJ= 10410.164563157052
iteration 32 OBJ= 10408.364552302961
iteration 33 OBJ= 10407.431920727997
iteration 34 OBJ= 10408.286189641487
iteration 35 OBJ= 10407.907347050501
iteration 36 OBJ= 10407.451608770069
iteration 37 OBJ= 10407.189482360372
iteration 38 OBJ= 10406.484357336147
iteration 39 OBJ= 10409.167125968375
iteration 40 OBJ= 10406.840873883246
iteration 41 OBJ= 10407.679485561714
iteration 42 OBJ= 10405.341101045238
iteration 43 OBJ= 10404.704382334516
iteration 44 OBJ= 10405.348023082915
iteration 45 OBJ= 10405.347406984720
iteration 46 OBJ= 10401.873473651774
iteration 47 OBJ= 10404.036204419035
iteration 48 OBJ= 10405.072916975221
iteration 49 OBJ= 10402.976628923887
Elapsed estimation time in seconds: 30420.73
iteration 50 OBJ= 10403.285958168881
#TERM:
OPTIMIZATION NOT TESTED
Thanks,
Pavel